Be Mindful of the Creepiness Factor: How Online Behavioral Advertising Affects Organizational Reputation
Vaia Stougiantziki 11107588 Master's Thesis
Graduate School of Communication Master's programme Communication Science
Supervisor: Theo Araujo 30.06.2017
Abstract
Targeting advertising techniques are increasingly used by marketers, as they have been proven to be more effective than generic advertisements. However, we proposed that the positive impact of tailoring on persuasiveness, may attenuate through the creepiness factor, which is the sense that marketers are tracking consumers online. This study examined the effect of behaviorally tailored advertisements on attitudes towards the ad, attitudes towards the brand and organizational reputation. We executed an online experiment, in which participants were exposed to tailored and non-tailored ads and their perceptions about them were assessed. In order to understand which factors drive consumer attitudes, we carried out three multiple regression models using PROCESS macro for SPSS. According to the findings, the participants attributed higher levels of perceived creepiness when confronted with behaviorally tailored ads that included information about their past online behavior. We also found that perceived creepiness has a direct, negative effect on attitudes towards the advertisement but it has no impact on attitudes towards the brand. It was also suggested that corporate branding strategy may differentiate the effect of tailored ads on organizational reputation. Theoretical and practical implications are discussed, along with suggestions for future research.
Keywords: targeting advertising, online behavioral advertising, perceived
creepiness, attitudes towards the ad, attitudes towards the brand, organizational reputation
Be Mindful of the Creepiness Factor: How Online Behavioral Advertising Affects Organization Reputation
Imagine you have browsed online for cars. Later that day, you visit Facebook and one of the advertisements on your home page features those same items. That is an explicit example of an online marketing technique known as online behavioral advertising (OBA), which refers to the practice of marketers keeping a record of consumers’ online behavior so to tailor the ads accordingly (Barnard, 2014; Ur, Leon, Cranor, Shay & Wang, 2012). That is only one of the various online advertising techniques that communication experts make use of in order to segment and target consumers more efficiently (Barnard, 2014).
Some of those targeting methods are technically simpler and others more advanced. A straightforward technique is contextual advertising, according to which the ad networks choose to present a commercial on a webpage based on its content, (Ur, et al., 2012; Malheiros, Jennett, Patel, Brostoff, & Sasse, 2012) in contrast to OBA, which is technologically more sophisticated and more effective (Ur et al., 2012; Malheiros et al., 2012). The more advanced a technique is, the better results it has on persuasion, affect and memory (Kalyanaraman & Sundar, 2006; Barnard, 2014). That is because the process of tailoring can help in making the ad more relevant to the recipient (Ur et al., 2012). On the other hand, an ad that is highly tailored to the consumer can lead him into perceiving it as creepy; the sense that marketers are tracking consumers online (Barnard, 2014). Thus, the present study argues that behaviorally tailored ads lead to higher levels of perceived creepiness (Barnard, 2014).
Most of the marketers believe that behavioral tailoring results in positive attitudes towards the ad and as a result enhances purchasing intentions (Oenema, Tan,
& Brug, 2005). However, there have been studies showing that too much tailoring can have the opposite results and lead to negative attitudes towards the ad, due to specific psychological mechanisms that make the consumer feel threatened (White, Zahay, Thorbjørnsen, & Shavitt, 2008) when realizing to have become too identifiable to the marketers (Barnard, 2014). With that being said, with the present study we argue that tailored communication results in negative attitudes towards the ad through the creepiness factor.
Previous studies have already proven the mediating role that perceived
creepiness plays on attitudes towards the ad (Barnard, 2014). What we aim to do with the present research is to add an extra step to the aforementioned logic by arguing that the attitude towards the brand will also be affected by the levels of perceived
creepiness. Keeping in mind that the content of the ad communicates much
information about the advertised brand and that this information helps in shaping an overall brand attitude (Campbell & Keller, 2003), we expect that consumers
confronted with tailored ads, other than having negative attitudes towards the ad, will have negative attitudes towards the advertised brand as well.
The main purpose of the study though is to examine the effect that tailored ads possibly have on corporate reputation. As stated earlier, ads are major sources of information about the brand (Campbell, & Keller, 2003). As a result, the attitudes shaped by the impressions and experiences with a brand help in brand awareness and in shaping the brand attitude accordingly (Hollis, 2005). Subsequently, corporate reputation is a result of stakeholder's experiences, relationships, and communications with the brand (Abratt & Kleyn, 2012). However, the corporate branding strategy that a company follows can work as a safeguard to reputation risks or contrariwise put the reputation of the whole organization at stake when bad news hit (Laforet & Saunders,
2005). The two corporate branding strategies we take under consideration is the house of brands strategy, where each product line carries its own brand name, unconnected to the parent company, and the branded house strategy, according to which all the launched products share the same brand name (Aaker & Joachimsthaler, 2000). The biggest advantage operating under a house of brands strategy is that mono brands help in limiting reputation loss (Laforet & Saunders, 2005). Therefore, we expect the negative effect of tailored ads on organizational reputation to be stronger for the brands that follow a branded house strategy.
The present study contributes to theory in several ways. Initially, it challenges the notion that targeting advertising results mainly in persuasion (Kalyanaraman & Sundar, 2006). Following, it draws on a relatively new concept that affects consumer attitudes towards the ad and the advertised brand: perceived creepiness (Barnard, 2014). Several qualitative studies (McDonald & Cranor, 2010; Ur et al., 2012) have long identified that tailoring techniques make the consumers realize that are being tracked by the marketers, but this is the second study that attempts to measure the effectiveness of the creepiness factor in an experimental setting. We also attempt to make a connection between behaviorally tailored ads and organizational reputation, by proposing that attitudes induced by ads affect the attitudes towards the advertised brand which result in changing stakeholders' overall evaluations of an organization.
This research also contributes to practice in three ways. Firstly, it tests once again the effectiveness of targeting advertising in our days, as it is an increasingly used practice by marketers. At the same time though, it calls them to re-examine the level of tailoring that an ad should undergo in order to diminish the negative effect that perceived creepiness might have on consumer attitudes. Secondly, it aims in revealing whether and how behaviorally tailored ads can help in brand building.
Thirdly, by understanding the effect that tailored ads might have on reputation, the practitioners can better design their communication messages to avoid elicit any negative attitudes that might affect consumer's evaluations about a company.
The two research questions that we aim to answer are: 1) To what extent behavioral tailored advertisements can negatively affect corporate reputation through the creepiness factor? and 2) To what extent this effect differs depending on the corporate branding strategy?
Literature Review Online behavioral advertising
The term of targeting advertising, which is usually preferred by
communication scholars (McDonald & Cranor, 2010; Ur et al., 2012; Malheiros et al., 2012; Smit, Van Noort, & Voorveld, 2014), refers to the ability of showing
advertisements to the most receptive audiences by making use of technological means (Chandra & Kaiser, 2014). One form of targeting advertising is online behavioral advertising (OBA), which will be the focus of the present study and refers to
advertisements that are served and relevant only to a certain individual (Malheiros et al., 2012; Barnard, 2014; Ur et al., 2012). Through the practice of collecting data about various individuals' online activities over time, advertisement networks create profile of Internet users which contain all the information about them (McDonald & Cranor, 2010; Malheiros et al., 2012). By interpreting the data, ad networks are in a position to tailor the advertisements in a way that it would be of most interest and relevance to those consumers (Ur et al., 2012; Barnard, 2014).
The practice of collecting data about online users starts the moment a user visits a web page. The content that a user is served with can come from the first party
provider, which would be the owner of the page, but also from third parties, by which we refer to external associates that collaborate with the first party provider (Ur et al., 2012; McDonald & Cranor, 2010).Third-parties can be advertising networks, analytic companies and/or social networks, such as Facebook and Twitter (Ur et al., 2012).
In order for both the first and the third party networks to keep track of a user's activities, a unique identifier has to be stored on his computer (Ur et al., 2012). The most common identifier is cookies which "are small text files that are put on users’ devices, such as notebooks or smart phones, to facilitate the functionality of a website (first-party, session or functional cookies) or to collect profile information for tailored advertising (third-party or tracking cookies)" (Smit et al., 2014, p.15). Cookies
facilitate in identifying the device (e.g. IP address) of the user without making associations with personally identifiable data, such as phone number (Agarwal, Shrivastava, Jaiswal, & Panjwani, 2013). Most importantly though, cookies help in tracking the user's browsing activity and usage patterns on site and on different websites that collaborate with the same third party agency (Ur et al., 2012; Backes, Kate, Maffei & Pecina, 2012; Agarwal et al., 2013). The collected data is used by third-party networks to draw inferences about age group, gender, and potential purchase interests so to tailor ads in a way that the user would be more likely to click on it (Agarwal et al., 2013; McDonald & Cranor, 2010). Therefore, the tailoring of the message might be based on simple demographic information, such as gender or race, known as demographic tailoring. Otherwise, an ad can undergo the process of
behavioral tailoring in order to reflect information of consumers past online behavior (e.g. browsed websites) (Barnard, 2014).
Effects of targeting. Targeting advertising can help in finding the users who would be more eligible on finding a certain ad interesting (Chandra & Kaiser, 2014).
It is obvious then that this technique is of great benefit to the advertisers (Farahat & Bailey, 2012). Previous research has shown that OBA can be more effective and increase click-through rates (CTR) when it is compared with generic advertisements' performance (Yan, Liu, Wang, Zhang, Jiang & Chen, 2009; Farahat & Bailey, 2012). The explanation for that is that through the process of targeting, the ads become more relevant to the interests of the consumer and therefore less annoying and/ or irritating. (Rohrer & Boyd, 2004; Kean & Dautlich, 2009).
Nevertheless, OBA is met with concern over the privacy of Internet users, as they are being tracked for the purposes of targeting (Agarwal et al., 2013). The extent to which the practice is desired by the users has been researched by a number of studies (McDonald & Cranor, 2010; Ur et al., 2012; Turow, King, Hoofnagle, Bleakley & Hennessy, 2009; Malheiros et al., 2012; Agarwal et al., 2013). A survey by Turrow and colleagues (2009) found that the majority of the respondents were worried about their privacy when referring to behaviorally tailored ads, even when the researchers clarify that the act of tracking is completely anonymous. Another study, demonstrated that user attitudes towards OBA are mixed (Ur et al., 2012). The respondents acknowledge the benefits of OBA, such as being served with relevant to their interests advertisements, yet they posed privacy dilemmas (Ur et al., 2012). Therefore, it comes as no surprise to say that privacy is the number one concern of users when talking about OBA.
On the other hand, some qualitative studies have revealed a relatively new concept, called "perceived creepiness", that is associated with highly tailored
advertisements. A scholar by McDonald and Cranon (2010) found that almost half of the participants (46%) claimed that it is creepy to have advertisements based on sites that have been visited before. In the same vein, the majority of the participants in
another research also perceived the idea of tailoring the advertisements as creepy (Ur et al., 2012). Lastly, an experimental study proved that behaviorally tailored ads were indeed resulting in higher levels of perceived creepiness than ads that were not
behaviorally tailored (Barnard, 2014). That said, this research will focus especially on the "creepiness factor", which in few words is the sense of marketers tracking an individual's online activities (Barnard, 2014).
Creepiness Factor. The "creepiness factor" has been defined as the sense that an individual is being watched, tracked or followed by marketers (Malheiros et al., 2012; Barnard, 2014). That feeling is present when a consumer receives tailored advertisements, in which the information presented is considered to be too personal or
too private (Barnard, 2014). At that moment, the consumer realizes that has become
too identifiable to marketers, since it becomes obvious that they use all the
information they have about him in order to adapt the advertisement in a way that it would only be relevant to him (Barnard, 2014; O'Donnell & Cramer, 2015).
An earlier study by Barnard (2014) revealed how perceived creepiness may lead to negative consumer reactions towards tailored ads. According to the findings , perceived creepiness leads to perceived threat or lack of control (Barnard, 2014). Perceived threat has been associated with reactance theory, which proposes that communication messages, that threaten an individual's freedom, tend to enact the mechanism of resisting or even rejecting the message (Brehm & Brehm, 1981). Morimoto and Chang (2006) explained that this action can be justified due to the sense of losing control over the choices an individual can make.
Perceived threat is followed by personalization reactance, a concept that was proposed by White and colleagues (2008) on a study about reactions to highly personalized emails. They proposed that personalization reactance occurs for two
reasons. Firstly, when the consumer thinks that there is insufficient justification of the use of someone's personal information for tailoring the communication messages. Secondly, when the perceived utility of the advertised good is low (White et al., 2008). In the same vein, Barnard (2014) has shown that reactance in behaviorally tailored advertisements works in the same way as it does in the contexts of
personalized emails (White et al., 2008). The researcher demonstrated that perceived creepiness led the participants feel that they lose control over the choices they have online (perceived threat), which resulted in greater reactance.
As a consequence, a consumer that realizes that the communication message he receives is highly individualized to him, exactly because he is being tracked by the marketers, tends to attribute that same message as "creepy" (Barnard, 2014). Once a communication message is perceived as creepy, negative feelings towards the practice of targeting advertising (Malheiros et al., 2012; McDonald & Cranor, 2010) and negative overall impression towards the ad come into play (Barnard, 2014). Thus, this study argues that behavioral tailored ads result in negative attitudes from the
consumers towards the ad, due to the creepiness factor. Thus, we articulate the following hypotheses:
H1. Behaviorally tailored advertisements will lead to higher levels of perceived
creepiness, compared to non-tailored ads.
H2a. In comparison to non-tailored ads, behaviorally tailored advertisements will
lead to more negative attitudes towards the ad, mediated by perceived creepiness.
Moreover, the content of the ad helps in communicating much information about the advertised brand which in return, helps in forming an overall brand attitude (Campbell & Keller, 2003). Especially when the consumer is unfamiliar with the
brand, it is more probable to base his evaluation towards the brand on attitudes shaped by the ad (Campbell & Keller, 2003). Other studies on traditional advertising have shown that when consumers attribute manipulative intent on the advertisement, other than negative attitudes towards the ad, negative attitude towards the brand are also elicited (Campbell, 1995). Such attributions of manipulative intent from the
advertiser's side, lower the persuasiveness of the advertisement and as a result, of the advertised brand as well (Campbell, 1995).We see then that ad-induced attitudes play a significant role in shaping brand attitudes (Stayman & Aaker, 1988). Therefore, we would expect that negative attributions to the advertised brand could be evoked when consumers are confronted with a behaviorally tailored advertisement. This leads us to the following hypothesis:
H2b. In comparison to non-tailored ads, behaviorally tailored advertisements will
lead to more negative attitudes towards the brand, mediated by perceived creepiness.
Corporate branding strategy and Reputation
As stated earlier, attitudes elicited by the ads become attitudes towards the brand (Campbell & Keller, 2003) which overall brand perceptions evolve into the reputation of an organization (Abratt & Kleyn, 2012). It has already been claimed that tailored ads elicit higher levels of perceived creepiness (Barnard, 2014). If then the consumer perceives a tailored ad as creepy, we expect him to have negative attitudes firstly towards the ad and secondly, towards the advertised brand. Subsequently, the overall impression that the consumer has about a company, the reputation of the organization, might also be affected. On the other hand, a brand name does not always mirror the name of the parent company. Several organizations, such as P&G, follow a corporate branding strategy according to which they have various independent
product brands with a distinct brand name that has little or no connection to the corporation (Aaker & Joachimsthaler, 2000; Uggla, 2006). In cases like this, the consumer might have a certain attitude towards the brand, but his attitude towards the parent company might be different. We see then that the corporate branding strategy under which a company operates its businesses, might differentiate the effect of negative attitudes obtained by a tailored ad on organizational reputation. In the following paragraphs, we aim to unfold the way in which behaviorally tailored ads can negatively affect organizational reputation, taking under consideration the corporate branding strategy that a company follows.
Corporate Branding Strategy. Van Riel and Van Bruggen ( 2002) have defined corporate branding strategy "as a systematically planned and implemented process of creating and maintaining a favorable reputation of an organization and its constituent elements, by sending signals to stakeholders using the corporate brand" (p. 242). In general, corporate reputation is a set of beliefs and perceptions about the organization, which influences stakeholders' views and behaviors towards it
(Fombrun, Gardberg, & Sever, 2000; Ponzi, Fombrun, & Gardberg, 2011; Abratt & Kleyn, 2012). Aaker and Joachmsthaler (2000) have proposed the so-called "brand relationship spectrum", which identifies four basic strategies and nine sub-strategies and is related to the driver role that brands play. The spectrum ranges from a "branded house strategy" (BH), according to which all the launched products should share the same brand name, to a "house of brands strategy" (HB), where each product line carries its own, stand-alone brand name, unconnected to all other brands (Aaker & Joachimsthaler, 2000). The brand style that a company chooses can help in
maintaining a favorable reputation across all businesses of the organization but can also put the reputation at stake when bad news hit (Laforet & Saunders, 2005).
House of brands strategy. The house of brand (or product brand) strategy involves an assortment of independent, stand-alone brands, with little link to the mother company or to each other (Uggla, 2006; Aaker & Joachimsthaler, 2000). An example of a company operating under this strategy would be Procter & Gamble (P&G), with numerous of major product brands , such as Crest, each of which having its own driver role and is developed for the exact product (Aaker & Joachimsthaler, 2000; Uggla, 2006).
Following such a strategy has its advantages, such as clearly positioning brands and dominating niche markets. Moreover, having multiple mono brands help in avoiding or minimizing channel conflicts, incompatible brand associations with new or existing offerings and attaching a brand name to consumer's mind for a specific product (e.g. Swiffer) (Aaker & Joachimsthaler, 2000). The biggest
advantage though of a house of brands strategy is that mono brands help in limiting reputation loss across a whole range of businesses (Laforet & Saunders, 2005).
Branded house strategy. In a branded house (or corporate brand) strategy, the master brand is fully visible to all stakeholders and plays a leading role in driving brand value (Uggla, 2006). All the businesses operate under one brand name, which maximizes clarity in stakeholders' minds, synergy in operations and brand leverage by capitalizing the pre-established brand equity and brand knowledge in consumer's memory (Uggla, 2006; Aaker & Joachimsthaler, 2000). On the other hand, corporate brands have a difficulty in maintaining an image that can stand across various markets (Aaker & Joachimsthaler, 2000). Above all though, the major risk that a corporate brand faces is in times of crisis: having a worldwide known brand name can jeopardize the reputation of the whole organization (Laforet & Saunders, 2005).
We see then that the level of a company's visibility in communication messages and the choice of corporate branding strategy can work as a safeguard to reputation risks (Laforet & Saunders, 2005). An organization that follow a HB strategy, can limit reputation loss easier than organizations with a BH strategy, as the parent company is less visible in the first case and so the organization as a whole might not get affected (Laforet & Saunders, 2005). Keeping also in mind that ad-induced attitudes help in shaping brand attitudes (Stayman & Aaker, 1988) which over time create the reputation of an organization (Fombrun et al., 2000; Ponzi et al., 2011; Abratt & Kleyn, 2012), we expect that the effect of tailoring on organizational reputation would be dependent on corporate branding strategy. To put it more explicitly, we assume that the negative effect of tailoring on reputation, due to perceived creepiness, will be moderated by the corporate branding strategy that an organization follows. Thus, we argue the following:
H3. Behaviorally tailored ads will have a stronger negative effect on organizational
reputation for companies that follow a BH strategy, in comparison to companies with
a HB corporate branding strategy, mediated through perceived creepiness.
Figure 1- Conceptual Model
H3 H2a H2b H1 Type of Ad Corporate Brand. Strategy Perceived creepiness
Attitude towards the Ad
Attitude towards the Brand
Organizational Reputation
Methods Design
To test the hypotheses, we conducted an online experiment. The study featured a 2 (type of advertisement: tailored ad vs. non-tailored ad) X 2 (corporate branding strategy: branded house vs. house of brands) between-subjects design to test the hypotheses, which was fully crossed. The participants were randomly assigned to one of the four experimental conditions and exposed to advertisements designed for the study.
Sample
The sample of the main study consisted of geographically distributed
participants (N= 169), recruited through Facebook. Fifteen participants were excluded from the initial sample, due to incomplete and / or invalid values in their answers.
The final sample was contained from 151 participants which consisted by 98 females (64.9%). The age of the participants ranged between 19 to 57 years (M= 27.93, SD =7.84). The education level of the respondents was mainly consisted by master's degree holders (N=64, 42.4%), followed by bachelor's degree holders (N=63, 41.7%), high school graduates (N=19, 12.6%) and doctoral degree holders (N=5, 3.3%). The majority of the respondents were paid employees (41.7%) and students (31.8%).
Stimuli
The two conditions were manipulated as followed: a) for the corporate branding strategy, a sponsored advertisement was created with a product placement by one of the two brands (BMW or Crest) accompanied by a scenario in which the respondents were called to imagine that have visited Facebook and one of the ads on the top right of their Facebook's home page was for the advertised brand; b) for the
type of ad, the presented advertisement was either tailored or not (i.e. addressing in the copy that the product has already been liked by a friend, or not) accompanied by a scenario in which the respondents were called to imagine that have already browsed online for the category of the advertised product (cars or toothpastes) and later that day while visiting Facebook one of the ads on the top right of their Facebook's home page was for the advertised brand. Every other element of the ads were held constant, to diminish any unwanted factor.
Pre-test
An online pilot study (N=18) was conducted in order to gain information about the stimuli material for the main study. The composition of the pre-test sample can be found in Appendix A (See Appendix A). The pilot study helped the researcher decide which two brands would be used in the stimuli and whether the stimuli used in the pre-test was perceived as tailored and non-tailored respectively, in order to be reused for the main study.
Attitudes towards the brand. The respondents were presented with a list of 10 companies, half of which were following a HB strategy and the rest of them a BH corporate branding strategy. They were asked to respond on a 5 item scale (See Appendix A) created by Pope, Voges, and Brown (2004) about their attitude towards the presented companies (α=.742, M=4.88, SD=.610). The two companies that had the highest overall means were the ones selected to be used for the stimuli. P&G, from the companies following a HB strategy, was the one that scored the highest (M=5.39,
SD= .73) and BMW was respectively the company that had the highest mean
(M=5.39, SD= .85) among the companies that follow a BH strategy.
Tailoring of the ad. The respondents were first exposed to the non-tailored ad and then to the tailored one, based on which replied on a scale of combined items
about the degree of tailoring of the advertisement (See Appendix A). A paired samples t-test was conducted to compare the differences between the means of the two groups. The generic ad was found to be less tailored by the participants (M=3.72,
SD= .80) than the tailored ad (M=5.24, SD= 1.06) and the difference was significant
(M= -1.52, t(17)= -5.06, p < .001). Procedure
The experiment was conducted online via Qualtrics. The participants were recruited through Facebook, either through a post in which the researcher was inviting people to participate in the study or through personal messages. Both in the post and the messages a link to the Qualtrics experiment was included, accompanied with a short description of the study. Participants were able to complete the survey at their best convenience.
In the beginning of the survey, the respondents were asked for their informed consent. Having agreed to the above, the participants were asked to respond on some demographic questions, to indicate the name of a friend and to carefully read the content of the following pages, as they would be asked to give feedback on a new Facebook design which was the cover story. After that, the participants were randomly assigned to one of the four experimental conditions. Subsequently, all respondents filled out the dependent measures in the following order: creepiness factor, attitude towards the ad and attitude towards the brand. After informing the participants about the two corporate branding strategies, the organizational reputation, was also measured. Two manipulation checks were conducted by the end of the study. Having completed the questionnaire, the respondents were thanked for their time and effort.
Measures. Table 1 includes the correlations of all the variables used for this study and the survey questionnaire can be found in the appendices (See Appendix B).
Dependent Measure. The following variables were used to test the hypotheses.
Organizational Reputation. To measure the organizational reputation, a four-item scale created by Ponzi, Fombrun and Gardberg (2011) was used. The four-items were administered on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). All items were loaded on the same factor (EV=3.14, R2 = 78.38), resulting in one scale score (α=.91, M = 4.98, SD =1.18).
Table 1. Correlations Table
Pearson's r Correlations (N=151) Reputat ion At Brand AtA Creepi ness Type of Ad Brand
Strategy PR PT AR CR PCI BF Trust
Gend er Age ES Reputation 1 AtBrand .361** 1 AtA .330** .542** 1 Creepiness -.013 -.126 -.091 1 Type of Ad .019 .009 .014 .463** 1 Brand Strategy .260** .071 .064 .051 .020 1 Personal Relevance (PR) -.220** -.132 -.357** -.189* -.115 -.012 1 Perceived Threat (PT) -.137 -.162* -.182* .361** .330** -.012 -.054 1 Affective Reactance (AR) -.278** -.342** -.330** .253** .158 -.154 .104 .509** 1 Cognitive Reactance (CR) .188* .410** .636** -.017 -.118 .072 -.196* -.158 -.307** 1 Product Category Involvemen t .085 .072 .033 .175* .101 -.148 -.412** .109 -.064 .043 1 Brand Familiarity (BF) .213** .160 .068 -.179* -.086 -.020 -.125 -.167* -.209* .108 .093 1 Trust .584** .478** .373** -.052 .030 .224** -.159 -.102 -.167* .223** .093 .296** 1 Gender .018 .081 .051 -.048 -.074 -.138 -.007 .071 .015 .057 -.058 -.102 -.104 1 Age .031 -.002 .060 -.104 .019 .034 .031 .068 -.070 .027 .062 .057 .068 -.136 1 Educational Status (ES) -.049 -.134 -.110 -.046 .102 -.108 .053 .117 .125 -.096 -.091 -.093 -.098 .043 .291** 1 Note: * p < .05, ** p < .01, *** p <.001
Perceived creepiness. The perceived creepiness was answered by the respondents on a seven-point scale, ranging from 1 = strongly disagree to 7 =
strongly agree (Barnard, 2014). All items were loaded on one factor (EV=3.14, R2 = 78.73) and the averaging of the individual items resulted in one scale score (α = .90,
M = 4.34, SD =.14).
Attitude toward the ad.. The attitudes toward the advertisement were
measured by 7-point semantic differential questions, adapted from Dillard and Shen (2005). The seven items loaded on the same factor (EV=5.15,R2=73.67) and the
averaging score resulted in one scale (α = .88, M = 43.74, SD =.09).
Attitude toward the brand. Items adapted from MacKenzie, Lutz and Belch (1986) were asked to measure the attitude toward the brand, by 7-point semantic differential questions. The three items loaded on the same factor (EV=2.58,R2=86.28)
and averaged in one scale score (α=.92, M = 4.56, SD =.11).
Control Variables. The study had several control variables, as the following concepts have been found to be associated with at least one of the variables under examination.
Personal Relevance of ad. A three-item scale was deployed to assess the personal relevance of the ad to the participants. The scale was constructed by Campbell and Wright (2008). The items ranged from 1=strongly disagree to 7=strongly agree. All items loaded in one component (EV=2.22, R2= 74.11).
Averaging the individual items resulted in a scale score (α=.82, M = 4.57, SD =.12). Perceived threat. To test if the ad was perceived as a threat, four items were adapted from Dillard and Shen (2005). Participants indicated their level of agreement on a 7-point Likert scale ranging from 1=strongly disagree to 7=strongly agree. All
four items loaded on one factor (EV=2.72,R2=68.11) and averaged in one scale score
(α= .84, M = 3.51, SD =.11).
Personalization Reactance. According to reactance theory studies, the state of reactance can be detected in both affect (anger arousal) and cognition (unfavorable thoughts). Therefore, reactance was measured with both measures:
Affective reactance. Anger arousal was assessed on a 7-point scale, created by
Barnard (2014), where 1 = none of this feeling and 7 = a great deal of this feeling. All items loaded on one factor (EV=3.16,R2=78.99) and averaged in one scale score
(α=.91, M = 2.95, SD =.13).
Cognitive reactance. Unfavorable thoughts were measured on a 7-point scale
(1 =strongly disagree, 7 = strongly agree). The participants were requested to express their level of agreement with three items adopted from Gardner (2010). The items loaded on one factor (EV=2.08,R2=69.32). The scores from the separate items were
averaged to calculate an overall score (α=.77, M = 3.90, SD =.09).
Product category involvement. The study measures consumer involvement with the category of the product, with a scale adopted by Mittal (1995), contained of three items varying on a seven-point Likert scale (1=strongly disagree, 7= strongly
agree). The three items loaded on one factor (EV=2.52,R2=84.18) and averaged in
order to calculate a scale with the overall score (α=.91, M = 4.26, SD =.12). Brand familiarity. Brand familiarity was measured on a three items scale, created by Kent and Allen (1994), ranging from 1=strongly disagree to 7= strongly
agree. All items loaded on the same component (EV=2.16, R2=72.29). Averaging the
individual items resulted in one scale score (α= .81, M = 4.10, SD =.13).
Trust in the retailer. A scale of four items was used to measure the trust in the retailer, created by Verhoef, Franses and Hoekstra (2002). The items were
administered on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The four items loaded on one factor (EV=2.76,R2=69.12) and averaged in one scale score
(α=. 85, M = 4.44, SD =.07). Manipulation checks
At the end of the survey, the respondents were asked to reply on two
manipulation check questions, in order to ensure whether the participants perceived the manipulation as intended. The respondents were asked to express their level of agreement on the tailoring of the ad on the same scale used for the pilot study (See Appendix A). The items were measured on a 7-point Likert scale, from 1 = strongly
disagree to 7 = strongly agree (α=. 72, M = 4.32, SD =1.22) . The last question was
an attention check, asking the participants to indicate if the ad was presenting a product either by Crest (P&G) or BMW on which all participants responded corrected (X2 (1, N = 151) = 151.00, p < .001).
Results Randomization checks
There was no significant difference between the four conditions with age (F(3, 147) = 1.51, p = .213, η= .00), gender (X2 (3, N = 151) = 4.73, p = .193), education level (X2 (9, N = 151) = 4.85, p = .847) and occupational status (X2 (15, N = 151) = 13.29, p = .585). For that reason, these variables were not held as control variables in the analyses.
Manipulation checks
An independent t-tests was conducted to assess whether the manipulation of the tailoring was succeeded. The t-test tells us the participants attributed greater level of tailoring to the tailored ad (M = 4.83, SE = .12) than to non-tailored ad (M = 3.81,
SE = .14) and the difference was statistical significant (t(149) = 5.64, p > .001,
d=.902).
Hypotheses testing
To test our hypotheses, we made use of PROCESS, a modeling tool for SPSS that integrates many of the functions of existing statistical tools for mediation and moderation analysis, while making use of regression-based paths and providing 95% confidence intervals (Hayes, 2012).
Behaviorally tailored ads and perceived creepiness. The first hypothesis stated that participants who were exposed to behaviorally tailored ads would experience higher levels of perceived creepiness (H1). The model as a whole was significant (F(9, 141) = 7.55, p< .001) and the independent variables predicted 33% (R2= .33) of the variance in the outcome variable, perceived creepiness. The findings showed that there is a significant, moderately strong relationship between the type of ad1 and perceived creepiness (b*= 1.25, t (141)= .25, p=<.001, 95% CI [.76, 1.75]) 2. Therefore, H1 can be accepted and indeed behaviorally tailored ads tend lead to higher levels of perceived creepiness than a non-tailored ones.
The mediating role of perceived creepiness. Having established the direct effect between the type of ad to perceived creepiness (H1), we proceeded into testing whether perceived creepiness would mediate the effects of type of ad on attitudes towards the ad (H2a) and attitudes towards the brand (H2b). To test the mediating role of perceived creepiness on the previously mentioned relationship, we used
1
Note: Type of Ad: 0=non-tailored, 1= Tailored.
2
Note: Regarding the control variables, none of them has a statistically significant influence on perceived
creepiness: personal relevance (b*= -.13, t= -1.46, p= .147, 95% CI [-.30, .04]), perceived threat (b*= .17, t= 1.55, p= .125, 95% CI [-.04, .36]), affective reactance (b*= .17, t= 1.79 , p= .076, 95% CI [-.02, .36]), cognitive reactance (b*= .13, t= 1.15 , p= .252, 95% CI [-.09, .35]), product category involvement (b*= .12, t= 1.41, p= .160, 95% CI [.05, .30]), brand familiarity (b*= .12, t= 1.43, p= .154, 95% CI [.27, .04]), trust (b*= .11, t= -.78, p= .440, 95% CI [-.39, .17]), branding strategy (b*= .30, t= 1.20, p= .230, 95% CI [-.19, .79]).
PROCESS macro Model 4, which tests at the same time both the direct and the indirect effects (Hayes, 2012).
Results for H2a, indicated that perceived creepiness was a significant predictor of attitudes towards the ad (t (140)= -2.11, p = .04, 95% CI [-.18, -.01]). It has to be reminded that the direct effect between the type of ad to the perceived creepiness had already been established (t (141)= .25, p <.001, 95% CI [.76, 1.75]).
Figure 2 below illustrates these interactions along with the unstandardized
coefficients. The type of ad was a significant predictor of attitudes towards the ad, after controlling for the mediator, perceived creepiness (t (140)=2.05, p=.04, 95% CI [.01, .59]), but the total effect was not statistically significant (t (141)=1.31, p = .19, 95% CI [-.09, .45]), indicating no mediation. The above was also confirmed by Sobel’s Z-test (Sobel’s Z= -1.92, p = .06). The indirect effect was also tested using a boostrap estimation approach with 5000 samples, showing that it is (0.28)(0.01) = -.12. The results thus rejected H2a, showing that perceived creepiness did not mediate the effect of behaviorally tailored advertisements on the attitudes towards the ad 3. Figure 2- Mediation Results for Hypothesis 2a (Unstandardized Coefficients)
3 Note: Concerning the covariates, it was found that four of them intervened on the total effects model.
Personal relevance (b*= -.19, t= -4.17, p< .001, 95% CI [-.29,-.10]), cognitive reactance (b*= .50, t= 8.33 , p< .001, 95% CI [.39, .63]), product category involvement (b*= -.10, t= -2.09, p=.039, 95% CI [-.19, -.01]) and trust (b*= .30, t= 3.99, p< .001, 95% CI [.16, .46]). -.09** 1.25** Type of Ad Perceived Creepiness Attitude towards the Ad .18 (.30**) F(9, 141) =19.27, R2= .55, p<.001 Notes: 1 N= 151 2 * p < .05, ** p< .001 3
In parenthesis is the unstandardized coefficient for the direct effect of type of ad to attitudes towards the ad.
A second PROCESS regression analysis was used to investigate the mediating role of perceived creepiness on the relationship between the type of ad and attitudes towards the brand (H2b). Findings indicated that neither the type of ad (t (140) = 1.32, p = .19, 95% CI [-.21, .04]) nor perceived creepiness were significant predictors of attitudes towards the brand (t (140) = -1.37, p = .176, 95% CI [-.21, .04]). On the other hand, the direct relationship between the type of ad to the perceived creepiness has already been established (t (141)= .25, p < .001, 95% CI [.76, 1.75]). Since, the two direct effects towards the outcome variable were both not significant, we cannot establish the mediated relationship. The aforementioned is confirmed by the total effect model: the type of ad was not a significant predictor of attitudes towards the ad, after controlling for the mediator, perceived creepiness (t = (141) .16, p = .39, 95% CI [-.21, .54]), indicating no mediation (See Figure 3). The above was also verified by Sobel’s Z-test (Sobel’s Z= -1.29, p= .20). The indirect effect was (-0.32)(0.03) = 0.27. The results thus rejected H2b, showing that that perceived creepiness did not mediate the effect of behaviorally tailored advertisements on the attitudes towards the brand4. Figure 3- Mediation Results for Hypothesis 2b (Unstandardized Coefficients)
4 Note:Only affective reactance (b*= -.20, t= -2.71 , p= .008, 95% CI [-.34, .05]), cognitive reactance
(b*= .32, t= 3.71 , p< .001, 95% CI [.15, .49]) and trust (b*= .60, t= 5.48, p < .001, 95% CI [.38, .81]) were found to intervene on the total effects model.
.16 (.27) -.09 1.25** Type of Ad Perceived Creepiness Attitude towards the Brand F(9, 141) =9.17, R2= .37, p<.001 Notes: 1 N= 151 2 * p < .05, ** p< .001 3
In parenthesis is the unstandardized coefficient for the direct effect of type of ad to attitudes towards the brand.
The moderating role of corporate branding strategy. In order to test the indirect effect of the type of ad on organizational reputation through perceived creepiness, moderated by corporate branding strategy (H3), PROCESS macro Model 14 was used. Firstly, we tested the direct relationship between the type of ad and perceived creepiness, which was once again statistically significant (t (140) = 5.31, p < .001, 95% CI [.84, 1.83]). Secondly, we tested the moderated mediated relationship using type of ad as the independent variable. The results for the main effects (See
Figure 4) revealed that none of the predictor variables had a statistical significant
effect on organizational reputation. From the control variables, only trust in the retailer was intervening on the total effect model (t (137) = 5.65, p < .001,95% CI [.39, .81]). The interaction effect (see Figure 4) of perceived creepiness and corporate branding strategy on organizational reputation did not yield a significant result (t (137) = -.06, p = .951, 95% CI [-.65, .61]). Therefore, H3 is rejected, behaviorally tailored ads do not have a stronger negative effect on organizational reputation for companies that follow a BH strategy, in comparison to companies with a HB corporate branding strategy, mediated through perceived creepiness.
Figure 4- Moderated Mediation Results for Hypothesis 3 (Unstandardized Coefficients)
F(13, 197) =7.33, R2= .41, p< .001
Notes:
1 N= 151
2 * p < .05, ** p< .001
3 In parenthesis is the unstandardized coefficient for the direct effect of corporate br.strategy on reputation. 4 Type of Ad: 0=non-tailored, 1= Tailored
Corporate Branding Strategy: 0= HB, 1= BH
.07 (-.01) -.01 1.33** Type of Ad Perceived Creepiness Organizational Reputation .03 Corporate Brand. Strategy
Discussion
The purpose of the study was to examine the impact of behaviorally tailored ads on organizational reputation, taking also under consideration the corporate branding strategy that a company follows. We wanted to explore whether perceived creepiness would play a mediating role on the effect of behaviorally tailored ads on attitudes towards the ad, as well as on attitudes towards the brand. We also suggested that the negative effect that behaviorally tailored ads would have on organizational reputation, due to perceived creepiness, would be stronger for companies that follow a BH strategy, in comparison to companies with a HB corporate branding strategy.
One of the key findings of this study was that the behaviorally tailored advertisement attributed higher levels of perceived creepiness; the participants felt that the advertisers were watching, tracking, observing and/or following them. This finding contributes to the theory, by proving that the newly proposed concept of perceived creepiness is indeed present when people are exposed to behaviorally tailored ads (McDonald & Cranor, 2010; Ur et al., 2012; Barnard, 2014). Moreover, it challenges the notion that behaviorally tailored ads have a de facto, positive effect on consumers' memory, affect and persuasion (Kalyanaraman & Sundar, 2006; Barnard, 2014). For that reason, both the researchers and the practitioners should re-examine if targeted ads have indeed a persuasive effect by taking under consideration the factors that attenuate it. Future studies should also compare different levels of tailoring, as the comparison in this study was only between tailored and non-tailored ads. By doing so, they could examine how much of tailoring is considered to be too much by the
consumers, which leads them into perceiving a tailored ad as creepy. They should also focus on how to eliminate or reduce the creepiness factor that tailored ads elicit in consumers' minds. That would be of interest especially to the marketers, as they spend
considerable amount of money on advancing targeting techniques and collect even more data about the consumers, with the presumption that tailoring an ad leads only to positive attitudes and boosts purchasing intentions. Finding a way in which creepiness factor can be eliminated, can help both in having better persuasive results and in preventing from losing a significant amount of advertising budget
We had hypothesized that perceived creepiness would mediate the relationship between behaviorally tailored ads and attitudes to the ad, but we found no statistical support for the mediation. A previous study had demonstrated that the effect of tailoring on attitudes towards the ad is mediated by perceived creepiness, as well as two other concepts (Barnard, 2014). That study had showed that behaviorally tailored ads elicited higher levels of perceived creepiness, which increased the feeling of perceived threat, which in return resulted in higher reactance that made the consumer have negative feelings about the ad (Barnard, 2014). In this study, although we controlled for the two other variables, we proposed that only perceived creepiness would mediate the relationship between the type of the ad and the attitudes towards it. That decision was made upon the effect that the two other concepts had on attitudes towards the ad as it was less than the effect of perceived creepiness. We speculated that the effect of behaviorally tailored ads on the attitude towards it could be mediated only through perceived creepiness. However, we propose to future researchers to re-examine either the proposed model as it is or by adding the two other concepts, as we have strong indications of the important role that perceived creepiness plays on attitudes towards the ad. Understanding the psychological mechanism that drives the attitudes of the consumers, would be of relevance to the theorists interested in tailored communications, as well as, to practitioners interested in investing in targeting
Despite the fact that no mediation was found, the results indicated two relationships that we had not suggested. Firstly, we found a direct, significant relationship between perceived creepiness and attitudes towards the ad. That
relationship had a negative direction, meaning that if the level of creepiness increases, the attitudes towards the ad tends to get more negative. That is a new finding, which comes in contrast to the previous mentioned study by Barnard (2014), in which the effect of creepiness on attitudes to the ads was found to be mediated by perceived threat and personalization reactance. With the present study, we demonstrated that the relationship between perceived creepiness and attitudes towards the ad is not
necessarily mediated by other concepts. That is a strong indication of the important role that perceived creepiness plays on attitudes, that future researchers should not neglect. Secondly, we found that behaviorally tailored ads have a positive effect on attitudes towards the ad, which confirms previous findings saying that behaviorally tailored ads can be more effective than generic advertisements (Yan et al., 2009; Farahat & Bailey, 2012). The explanation for that is that through the process of tailoring, the ads become more relevant to the interests of the consumer and therefore less annoying and/ or irritating (Rohrer & Boyd, 2004; Kean & Dautlich, 2009).
We wanted to extent the above presented logic by one step: we suggested that perceived creepiness may also affect the attitudes towards the brand. The results didn't support the assumption, indicating that consumer attitudes towards the brand do not work in the same way as they do for attitudes towards the ad. That might be due to the fact that both of the examined brands were well-known to the participants. The
familiarity with a brand is an important factor when a consumer makes inferences about the brand (Campbell & Keller, 2003). It is logical then that when the
existing knowledge and did not make big changes in their attitudes towards it (Hollis, 2005). Future studies could focus on fictitious or less known brands to measure whether there is an effect of tailoring on brand attitudes. That finding would be of great importance to the marketers that are interested in employing tailoring techniques for branding purposes. In case that there is an effect, they should design their strategic messages in a way that aims in changing the existing perceptions about the brand and elicits new brand attitudes (Hollis, 2005).
The main focus of the study though was to examine the effect of tailoring on organizational reputation, while comparing two corporate branding strategies and assuming that this relationship could be mediated by perceived creepiness. The hypothesis was not supported, showing that organizational reputation cannot easily get affected by one advertisement, even if that elicits negative ad attitudes. Although brand attitudes over time shape the reputation of an organization (Fombrun et al., 2000; Ponzi et al., 2011; Abratt & Kleyn, 2012), the results signify that brand
attitudes would be difficult to change by a single interaction with the brand (Campbell & Keller, 2003). In this research, we exposed the participants to only one
advertisement, in which a familiar brand was presented. Other than assuming that one ad is not enough to change the brand attitudes and so the reputation of an
organization, the effect of brand familiarity might have also attenuated the impact of tailoring on organizational reputation. Other studies should examine firstly the impact of brand familiarity on reputation and corporate branding strategy, and secondly, assess brand attitudes and reputation with repetitive measures.
Limitations and suggestions for future studies
The present study had some limitations. Firstly, the participants were exposed to a replica Facebook page that was created for the purposes of the study, rather than
an actual Facebook page where the participants could have seen a similar to the study ad including a friend's name in the content. That might had decreased the effect of creepiness on other variables. Future studies should explore the proposed conceptual model in a realistic setting, by making use of more sophisticated computer programs. Despite the fact that this may be a limitation, it is also important to note that even being exposed to a tailored ad on a replica Facebook page elicits higher levels of perceived creepiness than a non-tailored ad, as the manipulation check suggested . Secondly, the participants were presented with a hypothetical scenario in which they were asked to imagine that they had searched online for a specific product, which is against the actual practice of tailoring where marketers track the consumers on their own personal computer. The fact that they hadn't indeed searched for the product themselves might have lessened the effect of tailoring. Researchers interested in the topic should include products that the participants have actually searched for online. Thirdly, we had only two conditions of tailoring; tailored versus non- tailored ads. In the case of including ads that would reflect more information of the participant's past online behaviors, so more levels of tailoring, the effects of both tailoring and
creepiness would possibly be stronger. We would propose to the future researchers to include more types of tailored ads in their studies in order to test whether the effects of tailoring are consistent or maybe even stronger. Lastly, it would be interesting to test the effect of creepiness in other types of social networks or even on app stores, as there have been studies demonstrating that perceived creepiness may be elicited when mobile users realize that data are collected and distributed from smartphone apps as well (Shklovski, Mainwaring, Skúladóttir, & Borgthorsson, 2014).
References
Aaker, D. A., & Joachimsthaler, E. (2000). The brand relationship spectrum: The key to the brand architecture challenge. California management review, 42(4), 8-23. doi: 10.2307/41166051
Abratt, R., & Kleyn, N. (2012). Corporate identity, corporate branding and corporate reputations: Reconciliation and integration. European Journal of Marketing,
46(7/8), 1048-1063. doi: 10.1108/03090561211230197
Agarwal, L., Shrivastava, N., Jaiswal, S., & Panjwani, S. (2013, July). Do not
embarrass: re-examining user concerns for online tracking and advertising. In
Proceedings of the Ninth Symposium on Usable Privacy and Security (p. 8).
ACM. doi: 10.1145/2501604.2501612
Backes, M., Kate, A., Maffei, M., & Pecina, K. (2012, May). Obliviad: Provably secure and practical online behavioral advertising. In Security and Privacy
(SP), 2012 IEEE Symposium on (pp. 257-271). IEEE. doi: 10.1109/SP.2012.25
Baek, T. H., & Morimoto, M. (2012). Stay away from me. Journal of Advertising,
41(1), 59–76. doi:10.2753/JOA0091-336741010
Barnard, L. (2014). The cost of creepiness: How online behavioral advertising affects
consumer purchase intention. The University of North Carolina at Chapel Hill.
Retrieved from:
http://search.proquest.com/openview/9eed2964f83ae47aa4ef74b2968fdbd0/1? pq-origsite=gscholar&cbl=18750&diss=y
Brehm, S. S., & Brehm, J. W. (1981). Psychological reactance: A theory of freedom
and control. Academic Press.
Campbell, D. E., & Wright, R. T. (2008). Shut-up I don't care: Understanding the role of relevance and interactivity on customer attitudes toward repetitive online
advertising. Journal of Electronic Commerce Research, 9(1), 62. Retrieved from: http://web.csulb.edu/journals/jecr/issues/20081/Paper5.pdf
Campbell, M. C. (1995). When attention-getting advertising tactics elicit consumer inferences of manipulative intent: The importance of balancing benefits and investments. Journal of Consumer Psychology, 4(3), 225–254. doi:
10.1207/s15327663jcp0403_02
Campbell, M. C., & Keller, K. L. (2003). Brand familiarity and advertising repetition effects. Journal of consumer research, 30(2), 292-304. ISO 690. doi:
10.1086/376800
Chandra, A., & Kaiser, U. (2014). Targeted advertising in magazine markets and the advent of the internet Management Science, 60(7), 1829-1843. doi:
10.1287/mnsc.2013.1830
Dillard, J. P., & Shen, L. (2005). On the nature of reactance and its role in persuasive health communication. Communication Monographs, 72(2), 144–168. doi: 10.1080/03637750500111815
Farahat, A., & Bailey, M. C. (2012, April). How effective is targeted advertising?. In
Proceedings of the 21st international conference on World Wide Web (pp.
111-120). ACM. WWW 2012. doi: 10.1145/2187836.2187852
Fombrun, C. J., Gardberg, N. A., & Sever, J. M. (2000). The Reputation QuotientSM: A multi-stakeholder measure of corporate reputation. Journal of Brand
Management, 7(4), 241-255. doi:10.1057/bm.2000.10
Gardner, E. L. (2010). Ease the resistance: The role of narrative and
other-referencing in attenuating psychological reactance to persuasive diabetes
messages (Doctoral dissertation, University of Missouri--Columbia).
http://search.proquest.com/openview/6244bccab1f6727895aa6a2ebb427960/1 ?pq-origsite=gscholar&cbl=18750&diss=y
Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling [White paper]. Retrieved from http://www.afhayes.com/ public/process2012.pdf
Hollis, N. (2005). Ten years of learning on how online advertising builds brands.
Journal of advertising research, 45(2), 255-268. doi:
10.1017/S0021849905050270
Kalyanaraman, S., & Sundar, S. S. (2006). The psychological appeal of personalized content in Web portals: Does customization affect attitudes and behavior?
Journal of Communication, 56(1), 110–132. doi:
10.1111/j.1460-2466.2006.00006.x
Kean, A., & Dautlich, M. (2009). A guide to online behavioural advertising. Internet Advertising Bureau, London.
Kent, R. J., & Allen, C. T. (1994). Competitive interference effects in consumer memory for advertising: the role of brand familiarity. The Journal of
Marketing, 97-105. doi: 10.2307/1252313
Laforet, S., & Saunders, J. (2005). Managing brand portfolios: How strategies have changed. Journal of Advertising Research, 45(3), 314-327. doi:
10.1017/S0021849905050397
MacKenzie, S. B., Lutz, R. J., & Belch, G. E. (1986). The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing
explanations. Journal of marketing research, 130-143. doi: 10.2307/3151660 Malheiros, M., Jennett, C., Patel, S., Brostoff, S., & Sasse, M. A. (2012). Too close
personalized advertising. In Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems (pp. 579–588). New York, NY: ACM.
doi:10.1145/2207676.2207758
McDonald, A., & Cranor, L. F. (2010). Beliefs and behaviors: Internet users'
understanding of behavioral advertising. Retrieved from:
https://www.researchgate.net/profile/Lorrie_Cranor/publication/228237033_B eliefs_and_Behaviors_Internet_Users'_Understanding_of_Behavioral_Adverti sing/links/00b7d5319b862a63bb000000.pdf
Mittal, B. (1995). A comparative analysis of four scales of consumer involvement.
Psychology & Marketing, 12(7), 663-682. doi: 10.1002/mar.4220120708
Morimoto, M., & Chang, S. (2006). Consumers’ attitudes toward unsolicited commercial e-mail and postal direct mail marketing methods. Journal of
Interactive Advertising, 7(1), 1–11. doi:10.1080/15252019.2006.10722121
O'Donnell, K., & Cramer, H. (2015, May). People's Perceptions of Personalized Ads. In Proceedings of the 24th International Conference on World Wide Web (pp. 1293-1298). ACM. doi: 10.1145/2740908.2742003
Oenema, A., Tan, F., & Brug, J. (2005). Short-term efficacy of a web-based
computer-tailored nutrition intervention: main effects and mediators. Annals
of Behavioral Medicine, 29(1), 54-63. doi: 10.1207/s15324796abm2901_8
Ponzi, L. J., Fombrun, C. J., & Gardberg, N. A. (2011). RepTrak™ pulse:
Conceptualizing and validating a short-form measure of corporate reputation.
Corporate Reputation Review, 14(1), 15-35. doi: 10.1057/crr.2011.5
Pope, N. K. L., Voges, K. E., & Brown, M. R. (2004). The effect of provocation in the form of mild erotica on attitude to the ad and corporate image: Differences
between cause-related and product-based advertising. Journal of advertising,
33(1), 69-82. doi: 10.1080/00913367.2004.10639154
Rohrer, C., & Boyd, J. (2004, April). The rise of intrusive online advertising and the response of user experience research at Yahoo!. In CHI'04 Extended Abstracts
on Human Factors in Computing Systems (pp. 1085-1086). ACM. doi:
10.1145/985921.985992
Shklovski, I., Mainwaring, S. D., Skúladóttir, H. H., & Borgthorsson, H. (2014, April). Leakiness and creepiness in app space: Perceptions of privacy and mobile app use. In Proceedings of the 32nd annual ACM conference on
Human factors in computing systems (pp. 2347-2356). ACM. doi:
10.1145/2556288.2557421
Smit, E. G., Van Noort, G., & Voorveld, H. A. (2014). Understanding online
behavioural advertising: User knowledge, privacy concerns and online coping behaviour in Europe. Computers in Human Behavior, 32, 15-22. doi:
10.1016/j.chb.2013.11.008
Stayman, D. M., & Aaker, D. A. (1988). Are all the effects of ad-induced feelings mediated by ad? Journal of Consumer Research, 15, 368-373. doi:
10.1086/209173
Turow, J., King, J., Hoofnagle, C. J., Bleakley, A., & Hennessy, M. (2009).
Americans reject tailored advertising and three activities that enable it. doi: 10.2139/ssrn.1478214
Uggla, H. (2006). The corporate brand association base: A conceptual model for the creation of inclusive brand architecture. European Journal of Marketing,
Ur, B., Leon, P. G., Cranor, L. F., Shay, R., & Wang, Y. (2012, July). Smart, useful, scary, creepy: perceptions of online behavioral advertising. In proceedings of
the eighth symposium on usable privacy and security (p. 4). ACM. doi:
10.1145/2335356.2335362
Van Riel, C. B., & Van Bruggen, G. H. (2002). Incorporating business unit managers' perspectives in corporate-branding strategy decision making. Corporate
Reputation Review, 5(2-1), 241-251. doi: 10.1057/palgrave.crr.1540177
Verhoef, P. C., Franses, P. H., & Hoekstra, J. C. (2002). The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: does age of relationship matter?.Journal of the
Academy of Marketing Science, 30(3), 202-216. doi:
10.1177/0092070302303002
White, T. B., Zahay, D. L., Thorbjørnsen, H., & Shavitt, S. (2008). Getting too personal: Reactance to highly personalized email solicitations. Marketing
Letters, 19(1), 39–50. doi:10.1007/s11002-007-9027-9
Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., & Chen, Z. (2009, April). How much can behavioral targeting help online advertising?. In Proceedings of the
18th international conference on World wide web (pp. 261-270). ACM. doi:
Appendices
Appendix A. Pre-test sample composition and operationalization of the variables Pre-test sample composition
The sample (N=18) of the pilot study consisted of 9 males (50%) and 9 females (50%). The age of the participants varied from 20 to 31 years, with a mean age of 25.85 years (SD = 3.18). The education level of the respondents was mainly consisted by master's degree holders (N = 10, 55.6 %) and holders of bachelor's degree (N = 7, 38.90 %), followed by high school graduates (N = 1, 5.6 %).
Attitudes towards the brand. The list of 10 companies the participants were presented with contained the following ones: Procter & Gamble (P&G), Nestle, General Motors (GM), Uniliever, Inditex, FedEx, Harley Davidson, H&M and BMW. The first five companies follow a house of brands corporate branding strategy and second five follow a branded house corporate branding strategy. They were asked to respond on a 5 item scale created by Pope, Voges, and Brown (2004) about their attitude towards the presented companies. The used scale is composed of five, seven-point semantic-differential phrases: "I think that (company): 1. has good products (1) / does not have good products (7), 2. is well managed (1) / is not well managed (7), 3. is involved in the community (1) / is not involved in the community (7), 4. responds to consumer needs (1) / does not respond to consumer needs (7), 5. is a good company to work for (1) / is not a good company to work for (7)".
Tailoring of the ad. The scale used to measure the tailoring of the ad had items coming by two independent scales and a new item created by the researcher. To check the demographic tailoring, participants were asked to indicate their level of agreement with the following statements, adopted from Kalyanaraman & Sundar, 2006: "The ad was tailored according to my interests" and "The ad did not have
anything to do with me or my life" (reverse-coded). The behavioral targeting was measured with: "The ad featured a product I have seen in the past" and "I felt the advertisement targeted me based on my past browsing behaviors" (Barnard, 2014). To measure the tailoring based on friends/network preferences, the author created a new item: "I felt the advertisement targeted me based on my friends' past
purchases/browsing history". The statements were assessed on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). It has to be mentioned that the product featured in the pre-test was a fictitious one, firstly used by Barnard (2014).
The confirmatory factor analysis indicated that there are three components, and therefore, represent three distinct latent variables. The first component consisted of two items: "The ad was tailored according to my interests" and "The ad did not have anything to do with me or my life", which item was firstly recoded (EV1= 1.74,
R2= 34.80). The second component was featuring two items as well, "I felt the
advertisement targeted me based on my friends’ past purchases/browsing history" and “I felt the advertisement targeted me based on my past browsing
behaviors”(EV2=1.46, R2
= 29.29), and the last component was consisted by only one item, "The ad featured a product I have seen in the past” (EV3=1.09, R2
= 21.85). After a Direct Oblimin Rotation, we labeled the three components into 1) tailored and relevance to own self (alpha= .639), 2) past browsing behavior (alpha= .629) and 3) product seen in the past.
Appendix B. Questionnaire Main Study
Q1 Dear participants,
The aim of the present study is to see how people react to different types of information presented on social media sites, with special focus on sponsored advertisements. I would like to emphasize that there are no right or wrong answers to the questions posed in this study, I am simply interested in your own personal opinion.
First you are going to be asked some demographic questions. Later, you are kindly requested to read carefully the content of the presented pages, since there will be questions about the content after you are finished viewing them.
Thank you for your participation in this study, Vaia.
Q2 I hereby declare that I have been informed in a clear manner about the nature and method of the research, as described in the email or social media invitation for this study.
I agree, fully and voluntarily, to participate in this research study. With this, I retain the right to withdra w my consent, without having to give a reason for doing so. I am aware that I may
halt my participation in the experiment at any time.
If my research results are used in scientific publications or are made public in another way, this will be done such a way that my anonymity is completely safeguarded. My personal
data will not be passed on to third parties without my express permission.
If I wish to receive more information about the research, either now or in future, I can contact Vaia Stougiantziki, vaia.stougiantziki@student.uva.nl.
Should I have any complaints about this research, I can contact the designated
member of the Ethics Committee representing the ASCoR, at the following address: ASCoR secretariat , Ethics Committee, University of Amsterdam, Postbus 15793, 1001 NG Amsterdam; 020525 3680; asc or‐ secr‐ fmg@uva.nl
I understand the text presented above, and I agree to participate in the research study. (1) I do not agree to participate in the research study. (2)
